Even though these multi-column networks have achieved significant progress, they still suffer from several significant disadvantages, as demonstrated by Li et al. [1]}. First, it isn't easy to train the multi-column networks due to their complex structures. Next, using different branches but almost the same network structures inevitably leads to redundancy in information and waste in the consumption of parameters for density-level classifiers rather than the generation of final density maps. As with all the disadvantages, multi-column network architectures may be ineffective in a narrow sense. Thus it motivates many researchers to exploit simpler yet effective and efficient networks. Therefore, single-column network architectures are come out to cater to the demands of more challenging situations in crowd counting.
